Characterization and prediction of chemical functions and weight fractions in consumer products
Assessing exposures from the thousands of chemicals in commerce requires quantitative information on the chemical constituents of consumer products. Unfortunately, gaps in available composition data prevent assessment of exposure to chemicals in many products. Here we propose filling these gaps via...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2016-01-01
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| Series: | Toxicology Reports |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214750016300671 |
| _version_ | 1811230548521123840 |
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| author | Kristin K. Isaacs Michael-Rock Goldsmith Peter Egeghy Katherine Phillips Raina Brooks Tao Hong John F. Wambaugh |
| author_facet | Kristin K. Isaacs Michael-Rock Goldsmith Peter Egeghy Katherine Phillips Raina Brooks Tao Hong John F. Wambaugh |
| author_sort | Kristin K. Isaacs |
| collection | DOAJ |
| description | Assessing exposures from the thousands of chemicals in commerce requires quantitative information on the chemical constituents of consumer products. Unfortunately, gaps in available composition data prevent assessment of exposure to chemicals in many products. Here we propose filling these gaps via consideration of chemical functional role. We obtained function information for thousands of chemicals from public sources and used a clustering algorithm to assign chemicals into 35 harmonized function categories (e.g., plasticizers, antimicrobials, solvents). We combined these functions with weight fraction data for 4115 personal care products (PCPs) to characterize the composition of 66 different product categories (e.g., shampoos). We analyzed the combined weight fraction/function dataset using machine learning techniques to develop quantitative structure property relationship (QSPR) classifier models for 22 functions and for weight fraction, based on chemical-specific descriptors (including chemical properties). We applied these classifier models to a library of 10196 data-poor chemicals. Our predictions of chemical function and composition will inform exposure-based screening of chemicals in PCPs for combination with hazard data in risk-based evaluation frameworks. As new information becomes available, this approach can be applied to other classes of products and the chemicals they contain in order to provide essential consumer product data for use in exposure-based chemical prioritization. Keywords: Chemical function, Exposure modeling, Chemical prioritization, Consumer products, Cosmetics, ExpoCast |
| first_indexed | 2024-04-12T10:30:13Z |
| format | Article |
| id | doaj.art-4b7740d09057480598f1f564ed7109f0 |
| institution | Directory Open Access Journal |
| issn | 2214-7500 |
| language | English |
| last_indexed | 2024-04-12T10:30:13Z |
| publishDate | 2016-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Toxicology Reports |
| spelling | doaj.art-4b7740d09057480598f1f564ed7109f02022-12-22T03:36:51ZengElsevierToxicology Reports2214-75002016-01-013723732Characterization and prediction of chemical functions and weight fractions in consumer productsKristin K. Isaacs0Michael-Rock Goldsmith1Peter Egeghy2Katherine Phillips3Raina Brooks4Tao Hong5John F. Wambaugh6U.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United States; Corresponding author.Chemical Computing Group, Suite 910, 1010 Sherbrooke Street West, Montreal, QC H3A 2R7, CanadaU.S. Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United StatesOak Ridge Institute for Science and Education, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United StatesStudent Services Contractor, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United StatesICF International, 2635 Meridian Pkwy #200, Durham, NC 27713, United StatesU.S. Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, 109 T.W. Alexander Drive, Research Triangle Park, NC 27709, United StatesAssessing exposures from the thousands of chemicals in commerce requires quantitative information on the chemical constituents of consumer products. Unfortunately, gaps in available composition data prevent assessment of exposure to chemicals in many products. Here we propose filling these gaps via consideration of chemical functional role. We obtained function information for thousands of chemicals from public sources and used a clustering algorithm to assign chemicals into 35 harmonized function categories (e.g., plasticizers, antimicrobials, solvents). We combined these functions with weight fraction data for 4115 personal care products (PCPs) to characterize the composition of 66 different product categories (e.g., shampoos). We analyzed the combined weight fraction/function dataset using machine learning techniques to develop quantitative structure property relationship (QSPR) classifier models for 22 functions and for weight fraction, based on chemical-specific descriptors (including chemical properties). We applied these classifier models to a library of 10196 data-poor chemicals. Our predictions of chemical function and composition will inform exposure-based screening of chemicals in PCPs for combination with hazard data in risk-based evaluation frameworks. As new information becomes available, this approach can be applied to other classes of products and the chemicals they contain in order to provide essential consumer product data for use in exposure-based chemical prioritization. Keywords: Chemical function, Exposure modeling, Chemical prioritization, Consumer products, Cosmetics, ExpoCasthttp://www.sciencedirect.com/science/article/pii/S2214750016300671 |
| spellingShingle | Kristin K. Isaacs Michael-Rock Goldsmith Peter Egeghy Katherine Phillips Raina Brooks Tao Hong John F. Wambaugh Characterization and prediction of chemical functions and weight fractions in consumer products Toxicology Reports |
| title | Characterization and prediction of chemical functions and weight fractions in consumer products |
| title_full | Characterization and prediction of chemical functions and weight fractions in consumer products |
| title_fullStr | Characterization and prediction of chemical functions and weight fractions in consumer products |
| title_full_unstemmed | Characterization and prediction of chemical functions and weight fractions in consumer products |
| title_short | Characterization and prediction of chemical functions and weight fractions in consumer products |
| title_sort | characterization and prediction of chemical functions and weight fractions in consumer products |
| url | http://www.sciencedirect.com/science/article/pii/S2214750016300671 |
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